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Edge-Computing-Assisted Virtual Reality Computation Offloading: An Empirical Study
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DOI：10.1109/ACCESS.2022.3205120.png)
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Abstract
En 中文
Offloading heavy virtual reality (VR) computational operations to a network edge computation entity is receiving increasing attention as a tool to wirelessly and energy efficiently provide low-end client devices with high-quality and immersive interactive VR services anytime and anywhere across the globe. In this work, we aim to provide an understanding of various characteristics of VR computation offloading through comprehensive experiments conducted using a prototype testbed for edge-assisted VR processing and streaming. First, we investigate the benefits of VR offloading in terms of computational load and power consumption reduction for a client device compared to standalone operation. Next, we measure VR traffic patterns, including frame size and data and packet rates with various settings, such as different resolution and encoding options. We also measure several performance metrics associated with the quality of experience, namely, frame rate, packet loss rate, and image quality, with various configuration settings. Then, we present latency measurement studies and investigate per-component latency with various settings. Furthermore, we report the rigorous experiments performed to study the impacts of latency and motion patterns on the black borders formed due to image reprojection and the overfilling technique used to eliminate these black borders.
Keywords:
Headphones
Virtual reality
Graphics processing units
Traffic control
Streaming media
Image edge detection
Encoding
Edge computing
Virtual reality
edge computing
offloading
VR streaming
latency
overfilling
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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